SOTAVerified

Segmentation

Papers

Showing 27762800 of 13072 papers

TitleStatusHype
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and ColonoscopyCode1
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic SegmentationCode1
NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation PipelineCode1
NeRD: Neural Representation of Distribution for Medical Image SegmentationCode1
NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex ScenesCode1
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image SegmentationCode1
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Asymmetry Disentanglement Network for Interpretable Acute Ischemic Stroke Infarct Segmentation in Non-Contrast CT ScansCode1
NISF: Neural Implicit Segmentation FunctionsCode1
A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classificationCode1
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotationsCode1
nnU-Net: Self-adapting Framework for U-Net-Based Medical Image SegmentationCode1
Detecting Anomalies in Semantic Segmentation with PrototypesCode1
Constrained unsupervised anomaly segmentationCode1
A Character-Level Decoder without Explicit Segmentation for Neural Machine TranslationCode1
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image SegmentationCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic SegmentationCode1
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision TransformersCode1
Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19Code1
Boundary loss for highly unbalanced segmentationCode1
Dense Unsupervised Learning for Video SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1unSAM+ (Semi-supervised)Average Precision42.8Unverified
2SAMAverage Precision38.9Unverified
#ModelMetricClaimedVerifiedStatus
1HNN10%20Unverified
#ModelMetricClaimedVerifiedStatus
1ABANetF1 Score96.82Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50 + DeepLabV3+F1 score0.77Unverified
#ModelMetricClaimedVerifiedStatus
1LangGasIoU0.69Unverified